org.tribuo.common.tree

Class AbstractCARTTrainer<T extends Output<T>>

java.lang.Object

org.tribuo.common.tree.AbstractCARTTrainer<T>

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, DecisionTreeTrainer<T>, SparseTrainer<T>, Trainer<T>, WeightedExamples

Direct Known Subclasses:

CARTClassificationTrainer, CARTJointRegressionTrainer, CARTRegressionTrainer

public abstract class AbstractCARTTrainer<T extends Output<T>>
extends Object
implements DecisionTreeTrainer<T>

Base class for Trainer's that use an approximation of the CART algorithm to build a decision tree.

See:

 J. Friedman, T. Hastie, & R. Tibshirani.
 "The Elements of Statistical Learning"
 Springer 2001. PDF
 

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected static class	AbstractCARTTrainer.AbstractCARTTrainerProvenance Deprecated.

Field Summary

Fields

Modifier and Type	Field and Description
protected float	fractionFeaturesInSplit Number of features to sample per split.
protected int	maxDepth Maximum tree depth.
static int	MIN_EXAMPLES Default minimum weight of examples allowed in a leaf node.
protected float	minChildWeight Minimum weight of examples allowed in a leaf.
protected float	minImpurityDecrease Minimum impurity decrease.
protected SplittableRandom	rng
protected long	seed
protected int	trainInvocationCounter
protected boolean	useRandomSplitPoints Whether to choose split points for features at random.

Fields inherited from interface org.tribuo.Trainer

DEFAULT_SEED

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	AbstractCARTTrainer(int maxDepth, float minChildWeight, float minImpurityDecrease, float fractionFeaturesInSplit, boolean useRandomSplitPoints, long seed) After calls to this superconstructor subclasses must call postConfig().

Method Summary

Modifier and Type	Method and Description
float	getFractionFeaturesInSplit() Returns the feature subsampling rate.
int	getInvocationCount() The number of times this trainer instance has had it's train method invoked.
float	getMinImpurityDecrease() Returns the minimum decrease in impurity necessary to split a node.
boolean	getUseRandomSplitPoints() Returns whether to choose split points for features at random.
protected abstract AbstractTrainingNode<T>	mkTrainingNode(Dataset<T> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)
void	postConfig() Used by the OLCUT configuration system, and should not be called by external code.
TreeModel<T>	train(Dataset<T> examples) Trains a sparse predictive model using the examples in the given data set.
TreeModel<T>	train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a sparse predictive model using the examples in the given data set.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface com.oracle.labs.mlrg.olcut.provenance.Provenancable

getProvenance

Field Detail

MIN_EXAMPLES

public static final int MIN_EXAMPLES

Default minimum weight of examples allowed in a leaf node.

See Also:

Constant Field Values

minChildWeight

@Config(description="The minimum weight allowed in a child node.")
protected float minChildWeight

Minimum weight of examples allowed in a leaf.

maxDepth

@Config(description="The maximum depth of the tree.")
protected int maxDepth

Maximum tree depth. Integer.MAX_VALUE indicates the depth is unlimited.

minImpurityDecrease

@Config(description="The decrease in impurity needed in order to split the node.")
protected float minImpurityDecrease

Minimum impurity decrease. The decrease in impurity needed in order to split the node.

fractionFeaturesInSplit

@Config(description="The fraction of features to consider in each split. 1.0f indicates all features are considered.")
protected float fractionFeaturesInSplit

Number of features to sample per split. 1 indicates all features are considered.

useRandomSplitPoints

@Config(description="Whether to choose split points for features at random.")
protected boolean useRandomSplitPoints

Whether to choose split points for features at random.

seed

@Config(description="The RNG seed to use when sampling features in a split.")
protected long seed

rng

protected SplittableRandom rng

trainInvocationCounter

protected int trainInvocationCounter

Constructor Detail

AbstractCARTTrainer

protected AbstractCARTTrainer(int maxDepth,
                              float minChildWeight,
                              float minImpurityDecrease,
                              float fractionFeaturesInSplit,
                              boolean useRandomSplitPoints,
                              long seed)

After calls to this superconstructor subclasses must call postConfig().

Parameters:

maxDepth - The maximum depth of the tree.

minChildWeight - The minimum child weight allowed.

minImpurityDecrease - The minimum decrease in impurity necessary to split a node.

fractionFeaturesInSplit - The fraction of features to consider at each split.

useRandomSplitPoints - Whether to choose split points for features at random.

seed - The seed for the feature subsampling RNG.

Method Detail

postConfig

public void postConfig()

Used by the OLCUT configuration system, and should not be called by external code.

Specified by:

postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable

getInvocationCount

public int getInvocationCount()

Description copied from interface: Trainer

The number of times this trainer instance has had it's train method invoked.

This is used to determine how many times the trainer's RNG has been accessed to ensure replicability in the random number stream.

Specified by:

getInvocationCount in interface Trainer<T extends Output<T>>

Returns:

The number of train invocations.

getFractionFeaturesInSplit

public float getFractionFeaturesInSplit()

Description copied from interface: DecisionTreeTrainer

Returns the feature subsampling rate.

Specified by:

getFractionFeaturesInSplit in interface DecisionTreeTrainer<T extends Output<T>>

Returns:

The feature subsampling rate.

getUseRandomSplitPoints

public boolean getUseRandomSplitPoints()

Description copied from interface: DecisionTreeTrainer

Returns whether to choose split points for features at random.

Specified by:

getUseRandomSplitPoints in interface DecisionTreeTrainer<T extends Output<T>>

Returns:

Whether to choose split points for features at random.

getMinImpurityDecrease

public float getMinImpurityDecrease()

Description copied from interface: DecisionTreeTrainer

Returns the minimum decrease in impurity necessary to split a node.

Specified by:

getMinImpurityDecrease in interface DecisionTreeTrainer<T extends Output<T>>

Returns:

The minimum decrease in impurity necessary to split a node.

train

public TreeModel<T> train(Dataset<T> examples)

Description copied from interface: SparseTrainer

Trains a sparse predictive model using the examples in the given data set.

Specified by:

train in interface SparseTrainer<T extends Output<T>>

Specified by:

train in interface Trainer<T extends Output<T>>

Parameters:

examples - The data set containing the examples.

Returns:

A sparse predictive model that can be used to generate predictions for new examples.

train

public TreeModel<T> train(Dataset<T> examples,
                          Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Description copied from interface: SparseTrainer

Trains a sparse predictive model using the examples in the given data set.

Specified by:

train in interface SparseTrainer<T extends Output<T>>

Specified by:

train in interface Trainer<T extends Output<T>>

Parameters:

examples - the data set containing the examples.

runProvenance - Training run specific provenance (e.g., fold number).

Returns:

a predictive model that can be used to generate predictions for new examples.

mkTrainingNode

protected abstract AbstractTrainingNode<T> mkTrainingNode(Dataset<T> examples,
                                                          AbstractTrainingNode.LeafDeterminer leafDeterminer)